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1
Intro
2
Neural Substrate of Prediction Error
3
What are Computational Models?
4
Can Computational Models help?
5
Anatomy of Reinforcement Learning
6
Reinforcement Learning Circuitry
7
Reinforcement Learning in Depression
8
RL based Decision Making
9
RL heterogeneity and Neural Correlates
10
Identifying Structural Connectome of RL
11
Identifying Functional Connectome of RL
12
DSM-V Major Depressive Disorder
13
Can we use Reinforcement Learning to parse heterogeneity?
14
Parsing heterogeneity and Identifying biotypes
15
Multimodal Imaging Data fusion
16
Multimodal Data fusion Steps
17
Delay-Discount Factor
18
DLPFC Multimodal Brain Pattern (n=1103)
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the neural mechanisms of reinforcement learning in this 39-minute seminar by Dr. Poornima Kumar from McLean Hospital/Harvard Medical School. Delve into the functional and structural connectome underlying reinforcement learning processes. Examine computational models and their applications in understanding neural substrates of prediction error. Investigate the anatomy and circuitry of reinforcement learning, with a focus on its role in depression. Learn about reinforcement learning-based decision-making and its neural correlates. Discover methods for identifying structural and functional connectomes associated with reinforcement learning. Analyze the potential of reinforcement learning in parsing heterogeneity in major depressive disorder. Gain insights into multimodal imaging data fusion techniques and their application in identifying brain patterns related to reinforcement learning.

Functional and Structural Connectome of Reinforcement Learning

MGH Martinos Center
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